Model Comparison

Gemini 2.0 Flash vs Gemini 2.0 Flash ThinkingWhich is better in 2026?

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Verdict: Gemini 2.0 Flash vs Gemini 2.0 Flash Thinking — which is better?

Gemini 2.0 Flash (by Google) and Gemini 2.0 Flash Thinking (by Google) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Gemini 2.0 Flash outperforms in 0 benchmarks, while Gemini 2.0 Flash Thinking is better at 2 benchmarks (GPQA, MMMU). Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Choose Gemini 2.0 Flash if…

  • you want predictable pricing at $0.10/M input and $0.40/M output

Choose Gemini 2.0 Flash Thinking if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • you want the most recent training data — it shipped Jan 2025
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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemini 2.0 Flash outperforms in 0 benchmarks, while Gemini 2.0 Flash Thinking is better at 2 benchmarks (GPQA, MMMU).

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Thu Jun 18 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only Gemini 2.0 Flash specifies input context (1,048,576 tokens). Only Gemini 2.0 Flash specifies output context (8,192 tokens).

Google
Gemini 2.0 Flash
Input1,048,576 tokens
Output8,192 tokens
Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Thu Jun 18 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 2.0 Flash and Gemini 2.0 Flash Thinking support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Gemini 2.0 Flash

Text
Images
Audio
Video

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

Gemini 2.0 Flash

Proprietary

Closed source

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Release Timeline

When each model was launched

Gemini 2.0 Flash was released on 2024-12-01, while Gemini 2.0 Flash Thinking was released on 2025-01-21.

Gemini 2.0 Flash Thinking is 2 months newer than Gemini 2.0 Flash.

Gemini 2.0 Flash

Dec 1, 2024

1.5 years ago

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.4 years ago

1mo newer

Knowledge Cutoff

When training data ends

Both models have the same knowledge cutoff date of 2024-08-01.

They should have similar awareness of historical events and information up to this date.

Gemini 2.0 Flash

Aug 2024

Gemini 2.0 Flash Thinking

Aug 2024

Outputs Comparison

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Key Takeaways

Google
Larger context window (1,048,576 tokens)
Google
Higher GPQA score (74.2% vs 62.1%)
Higher MMMU score (75.4% vs 70.7%)
GoogleGemini 2.0 Flash
GoogleGemini 2.0 Flash Thinking
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Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 2.0 Flash
Google
Gemini 2.0 Flash Thinking

FAQ

Common questions about Gemini 2.0 Flash vs Gemini 2.0 Flash Thinking.

Which is better, Gemini 2.0 Flash or Gemini 2.0 Flash Thinking?

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks. Gemini 2.0 Flash is made by Google and Gemini 2.0 Flash Thinking is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemini 2.0 Flash compare to Gemini 2.0 Flash Thinking in benchmarks?

Gemini 2.0 Flash scores Natural2Code: 92.9%, MATH: 89.7%, FACTS Grounding: 83.6%, MMLU-Pro: 76.4%, EgoSchema: 71.5%. Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%.

What are the context window sizes for Gemini 2.0 Flash and Gemini 2.0 Flash Thinking?

Gemini 2.0 Flash supports 1.0M tokens and Gemini 2.0 Flash Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

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